Learning new skills in a data-efficient manner through real-world interaction is an open problem in robotics. The problem becomes even more challenging when a robot must ensure safety during interaction in the real world while learning a new skill. In this paper, we proposed a sim-to-real multi-goal learning algorithm called SafeAPT for safe robot learning in the real world. SafeAPT inherits the typical limitation of repertoire based learning, i.e., the pre-computed policies can be sub-optimal if the discretization of the goal space is not dense enough. Nevertheless, if further policy refinement is desired after performing SafeAPT, the fine-tuning of the policy can be performed on the parameter space safely using algorithms like Safe-Opt [1]}. We believe that sim-to-real learning approaches like SafeAPT can be useful in robot-learning applications where a small mistake by the robot can incur a high cost or a complete failure of the mission, e.g., in space or deep-sea applications.
